code: def test_NonFittableAggregator(self):
      for aggregator in list_NonFittableAggregator:
          # name must be of type str
          assert type(aggregator.__str__()) == str
  
          # Check if trainable is False, being a NonFittableAggregator
          assert not aggregator.trainable
  
          # predict on (sequence of) univariate series
          with pytest.raises(ValueError):
              aggregator.predict([self.real_anomalies])
          with pytest.raises(ValueError):
              aggregator.predict(self.real_anomalies)
          with pytest.raises(ValueError):
              aggregator.predict(
                  [self.mts_anomalies1, self.real_anomalies]
              )
  
          # input a (sequence of) non binary series
          with pytest.raises(ValueError):
              aggregator.predict(self.mts_train)
          with pytest.raises(ValueError):
              aggregator.predict([self.mts_anomalies1, self.mts_train])
  
          # input a (sequence of) probabilistic series
          with pytest.raises(ValueError):
              aggregator.predict(self.mts_probabilistic)
          with pytest.raises(ValueError):
              aggregator.predict(
                  [self.mts_anomalies1, self.mts_probabilistic]
              )
  
          # input an element that is not a series
          with pytest.raises(ValueError):
              aggregator.predict([self.mts_anomalies1, "random"])
          with pytest.raises(ValueError):
              aggregator.predict([self.mts_anomalies1, 1])
  
          # Check width return
          # Check if return type is the same number of series in input
          assert len(
              aggregator.eval_accuracy(
                  [self.real_anomalies, self.real_anomalies],
                  [self.mts_anomalies1, self.mts_anomalies2],
              )
          ), len([self.mts_anomalies1, self.mts_anomalies2])
  
file_path: darts/darts/tests/ad/test_aggregators.py
imports: from typing import Sequence
  import numpy
  import pytest
  from sklearn.ensemble import GradientBoostingClassifier
  from darts import TimeSeries
  from darts.ad.aggregators.and_aggregator import AndAggregator
  from darts.ad.aggregators.ensemble_sklearn_aggregator import EnsembleSklearnAggregator
  from darts.ad.aggregators.or_aggregator import OrAggregator
  from darts.models import MovingAverageFilter
label_desc: Use image node_id 4 for calling the TestADAggregators obj's underlying member method code with example usage: obj.test_NonFittableAggregator() without return types